Explainability Methods & Evaluation

This course explores advanced Explainable AI (XAI) techniques for interpreting and validating machine learning model behavior. It focuses on methods that move beyond simple feature importance toward mathematically grounded insights into black-box models. Through structured lessons and practical demonstrations, you will learn how Shapley theory underpins fair feature attribution, how SHAP methods generate local and global explanations, and how surrogate and rule-based approaches approximate model behavior. You will also work with counterfactual and contrastive explanations, including how to generate actionable alternatives and evaluate plausibility under perturbations and adversarial conditions. The course progresses from mathematical foundations to applied evaluation, emphasizing fidelity, faithfulness, stability, and reliability. Rather than treating explanations as visual outputs, it focuses on critically analyzing whether they accurately reflect model behavior. By the end of this course, you will be able to: - Explain the mathematical foundations of Shapley values and fair feature attribution - Apply SHAP techniques such as TreeSHAP, KernelSHAP, and interaction values - Design and evaluate surrogate and rule-based explanation methods - Generate and assess counterfactuals using practical evaluation metrics - Measure explanation quality through fidelity, faithfulness, stability, robustness, and sparsity - Test explanation reliability under perturbations and adversarial manipulation This course is ideal for machine learning engineers, AI researchers, data scientists, and professionals building trustworthy AI systems. A foundational understanding of ML concepts and Python-based model development is recommended; prior experience with explainability techniques is not required. Join us to learn how to design and validate XAI systems that deliver transparent, reliable insights into machine learning models.